Ever-changing conditions and emerging new challenges affect the ability of the healthcare sector to survive with the current system, and to maintain its processes effectively. In the healthcare sector, the conservation of the natural resources is being obstructed by insufficient infrastructure for managing residual waste resulting from single-use medical materials, increased energy use, and its environmental burden. In this context, circularity and sustainability concepts have become essential in healthcare to meliorate the sector’s negative impacts on the environment. The main aim of this study is to identify the barriers related to circular economy (CE) in the healthcare sector, apply big data analytics in healthcare, and provide solutions to these barriers. The contribution of this research is the detailed examination of the current healthcare literature about CE adaptation, and a proposal for a big data-enabled solutions framework to barriers to circularity, using fuzzy best-worst Method (BWM) and fuzzy VIKOR. Based on the findings, managerial, policy, and theoretical implementations are recommended to support sustainable development initiatives in the healthcare sector.

The objective of this study is to improve the efficiency of emergency departments (ED) by decreasing the overcrowding and patient waiting times. A hybrid methodology composed of a combination of lean techniques and discrete event simulation models was followed. Value stream mapping (VSM) was used as a lean technique with the aid of the ED staff. After obtaining the future state map of the ED, two discrete event simulation models were developed. By reviewing each triage process, the suggestions and proposed changes were incorporated into the future state map. There were five active process changes, each reducing lead and value added times by different amount. The usage of lean techniques can effectively reduce patient lead and value added times inside the ED, and when combined with discrete event simulation modeling, possible outcomes of the lean techniques can be tested and verified. This study contributed to the potential of lean techniques combined with simulation models in the case of reducing ED overcrowding and patient waiting times.

This paper aims to develop strategy and policy suggestions to increase the competitiveness of SMEs in the textile industry by analyzing the variables that affect competitiveness and contribute to competitiveness literature by adopting a holistic approach to the analysis of competitiveness variables. A hybrid model composed of Delphi and fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) methods were used to gather and analyze the competitiveness variables. This led to the identification of the 15 most important of 73 competitiveness variables relevant to SME competitiveness in the textile industry. These variables were analysed and ranked, and their causal relationships were mapped. The results obtained from the model may function as a reference for SME managers aiming to increase their firm’s competitive power.

This study addresses an integrated airport gate assignment and departure scheduling problem under capacity constraints while explicitly accounting for the operational role of apron resources. A bi-objective mixed integer linear programming model is developed to jointly determine gate or apron assignments and departure times by considering passenger transfer times, taxi operations, runway separation, and schedule deviations. The first objective minimizes a normalized composite measure of passenger transfer burden, taxi penalties, and departure schedule deviation, whereas the second objective minimizes apron usage. The epsilon constraint method is used to generate exact Pareto-efficient solutions. Computational experiments on synthetically generated congested hub airport instances with 20 flights show that increasing physical gate capacity from 3 to 5 improves the average value of Objective 1 from 1.37 to 0.92 and reduces average apron usage from 10.00 to 4.00 flights. In the highlighted 20-flight and 5-gate scenario, increasing apron usage from 3 to 5 assignments reduces the standard deviation of departure time deviations from 8.0 to 7.6 min. The results show that selective apron usage improves system-level schedule stability and that gate capacity and apron flexibility should be evaluated jointly in tactical airport planning.

This study investigates mean and volatility spillover effects among eight major cryptocurrencies; Bitcoin, Ethereum, Litecoin, Ripple, Stellar, Bitcoin Cash, Cardano and EOS utilizing VAR-BEKK-GARCH model. The results point out that there are bidirectional and unidirectional spillover effects among these major cryptocurrencies. Moreover, the findings indicate that some cryptocurrencies are the transmitter, while others act as a receiver and among all, Litecoin is the highest transmitter, and Stellar is the only one that acts as a receiver. The interdependence among cryptocurrencies supports that they are becoming more integrated and thereby, provides important investment strategies for investors and policy implications for regulators.